Quantifying the resilience of machine learning classifiers used for cyber security

The use of machine learning algorithms for cyber security purposes gives rise to questions of adversarial resilience, namely: Can we quantify the effort required of an adversary to manipulate a system that is based on machine learning techniques? Can the adversarial resilience of such systems be formally modeled and evaluated? Can we quantify this resilience such that different systems can be compared using empiric metrics? Past works have...